Systems and methods for performing inclusive indoor navigation

ABSTRACT

This disclosure relates to systems and methods for performing inclusive indoor navigation. State of the art systems and methods require extra hardware and fail to provide accurate localization and navigation with less precision. The method of the present disclosure obtains a nested environment data of a facility and estimate current spatial location of a user in the nested environment using surrounding recognition machine learning model. An optimal path categorized as convenient path, shortest path and multi-destination path from the current spatial location to a destination is determined. The current spatial location of the user is tracked on the optimal path using an augmented reality technique when navigation starts. The optimal path is dynamically updated based on feedback obtained from one or more user interaction modalities. The present disclosure provides user navigation with last meter precision, and no hardware and internet dependency.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:Indian provisional patent application no. 202021001718, filed on Jan.14, 2020. The entire contents of the aforementioned application areincorporated herein by reference.

TECHNICAL FIELD

The disclosure herein generally relates to field of indoor positioningsystem, and more particularly to system and method for performinginclusive indoor navigation for assisting physically challengedsubjects.

BACKGROUND

Indoor navigation including locating a user with high precision andfinding way to a desired destination in a new environment is achallenging task. Further, users with disabilities have more limitationsbecause of their physical challenges and hence there is a bigger needfor navigational assistance for disabled users than mainstream users.Global Positioning System (GPS) being most popular solution for outdoornavigation fails to show navigation accurately due to precision issuesand its more challenging for indoor as GPS cannot be used for indoorenvironment. Traditional systems which include Indoor Positioning System(IPS) provide turn by turn directional assistance using Bluetoothbeacons, RFID tags, Wi-Fi signature, GPS and geographic informationsystem (GIS) with IoT may fail to provide accurate localization andnavigation in real time as especially required by person withdisabilities.

SUMMARY

Embodiments of the present disclosure present technological improvementsas solutions to one or more of the above-mentioned technical problemsrecognized by the inventors in conventional systems. In an aspect, thereis provided a processor implemented method, the method comprising:obtaining, by one or more hardware processors, a nested environment dataof a facility under consideration for indoor navigation performed by auser, wherein the nested environment data is connected to a server, andwherein the nested environment data of the facility is obtained by:creating a two-dimensional digital map for each floor among a pluralityof floors of the facility, determining a nested map of the facility bysequentially arranging two-dimensional digital maps created for eachfloor of the facility, and performing map labelling to localize andcapture information of a plurality of landmarks in the nested map;receiving, by the one or more hardware processors, a destination withinthe facility from the user; estimating, using a surrounding recognitionmachine learning model implemented by the one or more hardwareprocessors, a current spatial location of the user with a predefinedprecision range at centimeter (cm) level by identifying (i) a userspecific area in the facility using the surrounding recognition machinelearning model trained with a plurality of real world images of thefacility and (ii) the current spatial location of the user with respectto the identified user specific area by triangulating input datareceived from a plurality of sensors; determining, by the one or morehardware processors, an optimal path from the current location to thedestination using the nested environment data, wherein the optimal pathfrom the current location to the destination is categorized as at leastone of (i) a convenient path (ii) multi-destination path, (iii) shortestpath in accordance with one or more user constraints; tracking, using anaugmented reality technique implemented by the one or more hardwareprocessors, the current spatial location of the user while usernavigates on the optimal path from the current location to thedestination; and dynamically updating, by the one or more hardwareprocessors, the optimal path from the tracked current spatial locationof the user to the destination based on feedback obtained from one ormore user interaction modalities.

In another aspect, there is provided a system, the system comprising:one or more data storage devices operatively coupled to one or morehardware processors and configured to store instructions configured forexecution via the one or more hardware processors to: obtain, by one ormore hardware processors, a nested environment data of a facility underconsideration for indoor navigation performed by a user, wherein thenested environment data is connected to a server, and wherein the nestedenvironment data of the facility is obtained by: creating atwo-dimensional digital map for each floor among a plurality of floorsof the facility, determining a nested map of the facility bysequentially arranging two-dimensional digital maps created for eachfloor of the facility, and performing map labelling to localize andcapture information of a plurality of landmarks in the nested map;receive, by the one or more hardware processors, a destination withinthe facility from the user; estimate, using a surrounding recognitionmachine learning model implemented by the one or more hardwareprocessors, a current spatial location of the user with a predefinedprecision range at centimeter (cm) level by identifying (i) a userspecific area in the facility using the surrounding recognition machinelearning model trained with a plurality of real world images of thefacility and (ii) the current spatial location of the user with respectto the identified user specific area by triangulating input datareceived from a plurality of sensors; determine, by the one or morehardware processors, an optimal path from the current location to thedestination using the nested environment data, wherein the optimal pathfrom the current location to the destination is categorized as at leastone of (i) a convenient path (ii) multi-destination path, (iii) shortestpath in accordance with one or more user constraints; track, using anaugmented reality technique implemented by the one or more hardwareprocessors, the current spatial location of the user while usernavigates on the optimal path from the current location to thedestination; and dynamically update, by the one or more hardwareprocessors, the optimal path from the tracked current spatial locationof the user to the destination based on feedback obtained from one ormore user interaction modalities.

In yet another aspect, there is provided one or more non-transitorymachine readable information storage mediums comprising one or moreinstructions which when executed by one or more hardware processorscauses: to: obtain, by one or more hardware processors, a nestedenvironment data of a facility under consideration for indoor navigationperformed by a user, wherein the nested environment data is connected toa server, and wherein the nested environment data of the facility isobtained by: creating a two-dimensional digital map for each floor amonga plurality of floors of the facility, determining a nested map of thefacility by sequentially arranging two-dimensional digital maps createdfor each floor of the facility, and performing map labelling to localizeand capture information of a plurality of landmarks in the nested map;receive, by the one or more hardware processors, a destination withinthe facility from the user; estimate, using a surrounding recognitionmachine learning model implemented by the one or more hardwareprocessors, a current spatial location of the user with a predefinedprecision range at centimeter (cm) level by identifying (i) a userspecific area in the facility using the surrounding recognition machinelearning model trained with a plurality of real world images of thefacility and (ii) the current spatial location of the user with respectto the identified user specific area by triangulating input datareceived from a plurality of sensors; determine, by the one or morehardware processors, an optimal path from the current location to thedestination using the nested environment data, wherein the optimal pathfrom the current location to the destination is categorized as at leastone of (i) a convenient path (ii) multi-destination path, (iii) shortestpath in accordance with one or more user constraints; track, using anaugmented reality technique implemented by the one or more hardwareprocessors, the current spatial location of the user while usernavigates on the optimal path from the current location to thedestination; and dynamically update, by the one or more hardwareprocessors, the optimal path from the tracked current spatial locationof the user to the destination based on feedback obtained from one ormore user interaction modalities.

In accordance with an embodiment of the present disclosure, the one ormore user constraints used for categorization of the optimal pathinclude user profiling information, information of landmark of interest,distance from an initial location of the user to the destination, andtime to reach the destination.

In accordance with an embodiment of the present disclosure, thepredefined precision range of the current spatial location of the useris 10 cm-20 cm.

In accordance with an embodiment of the present disclosure, the step oftracking includes avoiding disorientation and side wall bumping of theuser based on a deviation in direction of the user from a preplanneddirection on the optimal path.

In accordance with an embodiment of the present disclosure, the feedbackobtained from the one or more user interaction modalities fordynamically updating the optimal path includes feedback obtained fromthe obstacle detector, haptic feedback, voice instructions-basedfeedback, and visual interactions-based feedback.

In accordance with an embodiment of the present disclosure, the methodfurther comprising detecting one or more obstacles present on theoptimal path using an obstacle detector, wherein the obstacle detectordetects one or more obstacles using a combination of (i) computer visiontechniques utilizing a plurality of data continuously captured by acamera to detect the one or more obstacles with a first range of viewand (ii) one or more ultrasonic sensors to detect the one or moreobstacles with a second range of view, and wherein the plurality of datacontinuously captured by the camera helps in determining pattern of theone or more obstacles and is provided as input to the one or moreultrasonic sensors for prediction of one or more incoming obstaclesbased on the determined pattern.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate exemplary embodiments and, togetherwith the description, serve to explain the disclosed principles.

FIG. 1 illustrates an exemplary block diagram of a system for performinginclusive indoor navigation, in accordance with some embodiments of thepresent disclosure.

FIG. 2 is an exemplary flow diagram of a processor implemented methodfor performing inclusive indoor navigation, in accordance with someembodiments of the present disclosure.

FIG. 3 illustrates a non-limiting example of railway journey adaption tothe system and method for performing inclusive indoor navigation, inaccordance with some embodiments of the present disclosure.

FIG. 4 illustrates a non-limiting example of mall adapted to the systemand method for performing inclusive indoor navigation, in accordancewith some embodiments of the present disclosure.

FIG. 5 illustrates a non-limiting example of academic institutionsadapted to the system and method for performing inclusive indoornavigation, in accordance with some embodiments of the presentdisclosure.

It should be appreciated by those skilled in the art that any blockdiagrams herein represent conceptual views of illustrative systems anddevices embodying the principles of the present subject matter.Similarly, it will be appreciated that any flow charts, flow diagrams,and the like represent various processes which may be substantiallyrepresented in computer readable medium and so executed by a computer orprocessor, whether or not such computer or processor is explicitlyshown.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments are described with reference to the accompanyingdrawings. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears.Wherever convenient, the same reference numbers are used throughout thedrawings to refer to the same or like parts. While examples and featuresof disclosed principles are described herein, modifications,adaptations, and other implementations are possible without departingfrom the scope of the disclosed embodiments. It is intended that thefollowing detailed description be considered as exemplary only, with thescope being indicated by the following claims.

The embodiments herein provide a system and method for performinginclusive indoor navigation. In multiple scenarios, whenever a uservisit in any unfamiliar environment, it is a challenge to find the wayto desired destination. For example, if a person, who is visiting a mallfor the first time, wants to go to a specific store has to know wherethe store is, if it is open or if the store is too crowded. Similarly,in case of an airport, where a person is visiting for the first time andwants to explore the airport and at the same time keep track of theflight to catch. Also, in case of book exhibition where the user wantsto go to a particular author's stall, he needs to know one or moreinformative factors such as where the stall is, till when is it open andif the stall is too crowded so that he can explore the rest of the bookexhibition. Similarly, in case of an educational institute which mighthave multiple wings of different departments including physics,chemistry, a person may need to go to kinematics lab on second floor ofphysics department building. In case of hospital, if the user enters anunfamiliar hospital and goes to OPD (Outpatient Department) then ashe/she shares his/her case papers, he/she should be automatically guidedto the medical or to a lab for further checkup. However, different typeof challenging and complex situations are required to be addressed fordifferent types of users in indoor navigation. For example, for avisually challenged user, indoor navigation becomes more challengingsince the visually challenged user may require a specific directionalassistance with highest precision to stick to a path and avoid deviationfrom the path to reach the destination, along with surroundinginformation and obstacle detection. Similarly, a wheelchair bound usermay needs a path wide enough for the wheelchair to pass and the guidedpath should always avoid hurdles like staircase, stepping, and the like,even if the path is not shortest. Also, for an elderly user, a proactiveapproach is needed to guide the user through a path that address his oldage limitations.

State of the art systems and methods use Bluetooth beacon based indoorpositioning system (IPS) technology which gives an accuracy of about 2meters for localization which might be negligible for mainstream userbut for a person with disability (e.g., visually impaired), it creates ahuge difference as his/her whole turn would be changed by two meterswhile route requires precision in centimeters, a strategic positioningand dependency on extra hardware. Further, RFID based systems give anaccuracy of 20-50 cm and have lower proximity range wherein the userneeds to carry the RFID tag along. Furthermore, conventional Wi-Fisignature technology-based systems rely on signal strength at differentlocations in indoor environment, need extensive data collection and givean accuracy of about 2-3 meters. Traditional systems also utilizeaugmented reality (AR) for purpose of navigation wherein starting pointis kept fixed, localization is done using markers which may affect lookand feel of the indoor environment. However, for visually impairedusers, use of the augmented reality (AR) alone may not suffice inguiding the user to stick to a path without deviating and moving closeto the walls. Thus, state of the art methods lack in providing lastmeter precision, localization, require extra hardware or internetdependency.

The system of present disclosure enhances user experience and solveschallenges of people with disabilities while abiding by law and creatingdifferential experience. The system of present disclosure is an indoorpositioning system (IPS) which is user centric, inclusive and solveslast-meter localization problem with no hardware dependency which meansthere is no need of extra hardware to be setup in a target indoorenvironment. In context of the present disclosure, the term ‘inclusive’is used because of the broader reach of the system of the presentdisclosure addressing physical limitations of a person with disabilitylike wheelchair bound, visually impaired, elderly people and the like.Further, the system of the present disclosure include mainstream usersalso and bring them to a level ground in terms of navigation in anenvironment. Further, the system of present disclosure performs indoornavigation with on device path planning, user localization withoutinternet and by utilizing capabilities of augmented reality (AR),computer vision (CV), machine learning (ML), and artificial intelligence(AI) based techniques. The device herein may be a personal digitalassistant (PDA), a tablet a mobile or the like. The augmented reality(AR) based technique provides an integration of accelerometer, gyroscopeand camera which is used for identifying spatial position and trackingof the user in real time as he/she moves in the indoor environment. Thecomputer vision (CV) and the machine learning (ML) based techniques areused for map creation, localization and obstacle detection. In theproposed disclosure, the artificial intelligence (AI) based techniquesare used for creating user profiling information (alternatively referredas user persona) with real time user attributes and pre-fed basicdetails of the user such as name, age, and the like. Further, theartificial intelligence based techniques are used for determiningoptimal path including a convenient path that can have multipledestinations for routing disabled user (e.g., a wheelchair bound useralong a path wide enough for smooth wheelchair passage avoiding all thehurdles like staircase, stepping). While, convenient path for amainstream user in a crowded place takes the user from one destinationto the other keeping reference of crowd from minimum to maximum. Furtherthe optimal path includes multi-destination path (in case of museums,amusement parks) and shortest path.

Referring now to the drawings, and more particularly to FIG. 1 throughFIG. 5 , where similar reference characters denote correspondingfeatures consistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

FIG. 1 illustrates an exemplary block diagram of a system for performinginclusive indoor navigation, in accordance with some embodiments of thepresent disclosure. In an embodiment, the system 100 includes one ormore processors 104, communication interface device(s) or input/output(I/O) interface(s) 106, and one or more data storage devices or memory102 operatively coupled to the one or more processors 104. The one ormore processors 104 that are hardware processors can be implemented asone or more microprocessors, microcomputers, microcontrollers, digitalsignal processors, central processing units, state machines, graphicscontrollers, logic circuitries, and/or any devices that manipulatesignals based on operational instructions. Among other capabilities, theprocessor(s) are configured to fetch and execute computer-readableinstructions stored in the memory. In the context of the presentdisclosure, the expressions ‘processors’ and ‘hardware processors’ maybe used interchangeably. In an embodiment, the system 100 can beimplemented in a variety of computing systems, such as laptop computers,notebooks, workstations, mainframe computers, servers, a network cloud,hand-held devices such as mobile devices (e.g. smartphones) and thelike. In an embodiment, the processor 104, the I/O interface 106, andthe memory 102, may be coupled by a system bus.

The I/O interface 104 may include a variety of software and hardwareinterfaces, for example, a web interface, a graphical user interface,and the like. The interfaces 104 may include a variety of software andhardware interfaces, for example, interfaces for peripheral device(s),such as a keyboard, a mouse, an external memory, a camera device, and aprinter. The interfaces 104 can facilitate multiple communicationswithin a wide variety of networks and protocol types, including wirednetworks, for example, local area network (LAN), cable, etc., andwireless networks, such as Wireless LAN (WLAN), cellular, or satellite.For the purpose, the interfaces 104 may include one or more ports forconnecting a number of computing systems with one another or to anotherserver computer. The I/O interface 104 may include one or more ports forconnecting a number of devices to one another or to another server.

The memory 102 may include any computer-readable medium known in the artincluding, for example, volatile memory, such as static random accessmemory (SRAM) and dynamic random access memory (DRAM), and/ornon-volatile memory, such as read only memory (ROM), erasableprogrammable ROM, flash memories, hard disks, optical disks, andmagnetic tapes. In an embodiment, one or more modules (not shown) of thesystem 100 can be stored in the memory 102. The one or more modules (notshown) of the system 100 stored in the memory 102 may include routines,programs, objects, components, data structures, and so on, which performparticular tasks or implement particular (abstract) data types. In anembodiment, the memory 102 includes a data repository 110 for storingdata processed, received, and generated by the one or more hardwareprocessors 104.

The data repository 110, amongst other things, includes a systemdatabase and other data. In an embodiment, the data repository 110 maybe external (not shown) to the system 100 and accessed through the I/Ointerfaces 104. The memory 102 may further comprise informationpertaining to input(s)/output(s) of each step performed by the processor104 of the system 100 and methods of the present disclosure. In anembodiment, the system database stores information being processed ateach step of the proposed methodology. The other data may include, datagenerated as a result of the execution of the one or more hardwareprocessors 104 and the one or more modules (not shown) of the system 100stored in the memory 102.

The system 100 further includes a plurality of sensors 108 whichincludes an accelerometer, a gyroscope, a magnetometer, a camera, anultrasonic sensor, a Bluetooth, a GPS (global positioning system), anobstacle detector, a Wi-Fi sensor, and the like. In an embodiment, theobstacle detector refers to a small device that could be connected to ahandheld device such as smartphone and can be carried by user in pocket.

In an embodiment, the system 100 of the present disclosure can beconfigured to reduce the manual intervention. A detailed description ofthe above-described system and method for performing inclusive indoornavigation is shown with respect to illustrations represented withreference to FIG. 1 through FIG. 5 and use case examples.

FIG. 2 , with reference to FIG. 1 , is an exemplary flow diagram of aprocessor implemented method for performing inclusive indoor navigation,using the system 100 of FIG. 1 , in accordance with some embodiments ofthe present disclosure. In an embodiment, the system 100 includes one ormore data storage devices or memory 102 operatively coupled to the oneor more processors 104 and is configured to store instructionsconfigured for execution of steps of the method 200 by the one or moreprocessors 104. The steps of the method 200 will now be explained indetail with reference to the components of the system 100 of FIG. 1 .Although process steps, method steps, techniques or the like may bedescribed in a sequential order, such processes, methods and techniquesmay be configured to work in alternate orders. In other words, anysequence or order of steps that may be described does not necessarilyindicate a requirement that the steps be performed in that order. Thesteps of processes described herein may be performed in any orderpractical. Further, some steps may be performed simultaneously.

Referring to FIG. 2 , at step 202, the one or more processors 104 areconfigured to obtain a nested environment data of a facility underconsideration for indoor navigation performed by a user. In anembodiment, the facility under consideration may include but not limitedto a school building, a mall, a hospital, railway stations, airports,and/or the like. The nested environment data of the facility is obtainedby creating a two-dimensional digital map for each floor among aplurality of floors of the facility, determining a nested map of thefacility by sequentially arranging two-dimensional digital maps createdfor each floor among a plurality of floors of the facility, andperforming map labelling to localize and capture information of aplurality of landmarks in the nested map. In an embodiment, prior tocreation of the two-dimensional digital map, floor exit maps of a givenfloor of the facility are evaluated against an evaluation criterion. Inan embodiment, the evaluation criterion is used to check real worldarchitectural details of the facility such as dimension-based precisionof an existing map of the facility and positioning of landmarks insurrounding environment with respect to information provided by aclient. Here, the term ‘client’ is different from the user and isreferred for a person who wants the indoor navigation to be performed inhis/her facility. Here, the existing map is different from thetwo-dimensional digital map that is created. Further, if the floor exitmaps do not meet the evaluation criteria then their respectivetwo-dimensional (2D) digital maps are created and arranged as per theirsequence of existence to create the nested map of the facility. Here,the sequence of existence means first ground floor then first floor asso on. Further, map labelling (alternatively referred as landmarking) isperformed on respective map of the nested maps. Here, map labellingrefers to adding details to different landmarks with respect to thecreated two-dimensional digital map along with noting/capturingsurrounding information of the landmark including its unique aspectssuch as presence of a fountain, a picture, and/or the like. Here, thedetails of the landmarks may include but not limited to how thesurroundings are, name of the landmark, and/or the like. Also, eachlandmark is tagged to a unique reference surroundings for identificationbased on one or more parameters such as images of the landmark, text orsignages present, direction of the landmark, additional images like QRcodes or pictures, Wi-Fi signal strength and magnetic field intensity ofthe area. These one or more parameters distinguish one landmark fromother landmarks. Furthermore, direction of indoor environment such asgeospatial orientation and GPS location are captured to tag the nestedenvironment data to respective geographic location. Further, thesedetails are used in giving feedback to the user and in givingenvironmental information to the user. In an embodiment, the nestedenvironment data is connected to a server or a local device such asmobile phone which makes the system ready for plug and play for indoornavigation application.

Further, as depicted in step 204 of FIG. 2 , the one or more processors104 are configured to receive a destination within the facility from theuser. For example, in a mall, if the user is standing at ground floor,the destination could be a specific store at third floor. Furthermore,as depicted in step 206 of FIG. 2 , the one or more processors 104 areconfigured to estimate using a surrounding recognition machine learningmodel implemented by the one or more hardware processors, a currentspatial location of the user with a predefined precision range atcentimetre (cm) level by identifying (i) a user specific area in thefacility using the surrounding recognition machine learning modeltrained with a plurality of real world images of the facility and (ii)the current spatial location of the user with respect to the identifieduser specific area by triangulating a plurality of input data receivedfrom a plurality of sensors. The surrounding recognition machinelearning model utilizes a camera to capture real images of surroundingof the facility to identify an area within the facility where the useris present. Further, a plurality of data from the plurality of sensorssuch as magnetometer, Wi-Fi sensors, and/or the like is captured toidentify exact location of the user within the identified area. With theabove input data, each parameter act like a validation point to localizethe user. The validation points are the then continuously captured forthe user specific area finding and then for further positioning of theuser. In an embodiment, the surrounding information of the facility mayalso include but not limited to out of bound areas, distance betweeneach identified landmark in the nested map. For example, a user enters apremise, then the system of the present disclosure captures thesurrounding information of the user using a smartphone camera along withadditional parameters such as text, direction, Wi-Fi signal strength andthe like for unique identification of user location with respect to theenvironment.

In an embodiment, GPS location of the user is determined before thenavigational session starts. The GPS is used to determine the locationof the user to load corresponding nested environment data to the serverfor indoor navigation but not used during the indoor navigationalsession as it is inaccurate indoors. In other words, the GPS serves apurpose of giving approximate location of the user in order to determinewhich indoor area the user is entering which is further used forreference to load respective nested environment data to the server orthe local device. For example, in a large academic institute, the GPSlocation is used before the user enters the campus to load therespective nested environment data of the whole campus and then asbuilding in large campuses are kilometres away from each other, thusonce the user reaches close to the destination building based on theGPS, the respective building nested environment data is loaded.

In an embodiment, the nested environment implementation can be furtherunderstood with example of a hotel where the user wants to go fromground floor to a room on third floor of the hotel. First, as per GPScoordinates, the nested environment data is loaded (locally or viainternet) to the server or the local device from which the groundfloor's environment data will be loaded. Then, as navigational sessionstarts from the ground floor, the user is guided to stairs or lift toreach the third floor. On reaching the third floor, its environment datais loaded for further navigation to a specific room. Thus, the combinedenvironment data received from the ground floor to the third floor isreferred as the nested environment data. In an embodiment, use of GPS isnot confined to a location before user enters the environment but it hasa broader use to establish a relationship, wherein the relationshipcould be use of GPS with respect to a country, a city in the country, acampus in the city, a building in the campus and so on. The abovementioned relationship between country, city, building and campus isfacilitated by using the GPS. For example, railways or big corporates atcountry level, multiple campus within a city relationship or multiplebuildings in a campus relationship. Further, the GPS helps in scaling aproject to as big as country level or city level. For example, in caseof railways, the platform where the user is present is determined by afloor using nested environment data and which city the user is presentis determined by the GPS. Also, back tracing of the two-dimensionaldigital map of indoor environment of the facility under considerationcan be performed. For example, the back tracing can be performed fromthe two-dimensional digital map of the indoor environment of thefacility, then to the respective building, then to the city, and then tothe country, based on the relationship established by using the GPS.

Referring back to FIG. 2 , at step 208, the one or more processors 104are configured to determine an optimal path from the current location tothe destination using the nested environment data. In an embodiment, theoptimal path could be determined using but not limited to a weighteddirect path planning technique. In an embodiment, the optimal path fromthe current location to the destination is categorized as at least oneof (i) a convenient path (ii) multi-destination path, and (iii) shortestpath in accordance with one or more user constraints. In an embodiment,the one or more user constraints used for categorization of the optimalpath include user profiling information, information of landmark ofinterest, distance from an initial location of the user to thedestination, and time to reach the destination. In context of thepresent disclosure, the expression ‘user profiling information’ refersto details of physical appearance of the user and is indicative ofwhether the user is a physically fit person without any disability, or aperson with disabilities such as a visually challenged person, wheelchair bound person, a deaf person, a pregnant person, an elderly personand/or the like. In an embodiment, the user profiling information isgenerated by capturing a plurality of data pertaining to the user inreal time using a camera, a plurality of sensors, and/or the like. Forexample, picture of the user is captured that can detect age, or detectif the user uses cretches or if the user is sitting on a wheelchair. Thecaptured plurality of data is used by a user profiling machine learningmodel to generate the user profiling information in real time along witha pre-fed information such as name, age, gender of the user in thesystem. In an embodiment, the user profiling machine learning modelcould be but not limited to a deep learning based convolutional neuralnetwork built on top of MobileNet Architecture. Over time, the userprofiling machine learning model auto tunes itself as per userpreferences as well. In an embodiment, the convenient path refers to apath with multiple destinations. The user constraints that areconsidered for selection of convenient path include user profilinginformation, minimum time and minimum distance. For a disabled person(e.g. wheelchair bound user), convenient path refers to a path which iswide enough for providing smooth wheelchair passage while routing andhelp in avoiding all hurdles like staircase, stepping and/or the like.Whereas, for a mainstream user, the convenient path refers to a path ina crowded place which takes the user from one destination to otherkeeping reference of crowd from minimum to maximum. In an embodiment,the multi-destination path refers to a path that is longest with respectto time and distance, but it covers all important landmarks withinindoor environment. Thus, the user constraints that are considered forselection of multi-destination path include landmark of interest. Forexample, in a museum, a person is taken along a path that covers allattractions of the museum. In an embodiment, the shortest path refers toa path that is shortest with respect to time and distance from start toend in terms of distance. The user constraints that are considered forselection of shortest path include time, user profiling information anddistance. For example, in case of a hospital, the path given to amainstream user will be directly from entry to emergency ward.

Further, as depicted in step 210 of FIG. 2 , the one or more hardwareprocessors 104 are configured to track, using an augmented realitytechnique, the current spatial location of the user while user navigateson the optimal path from the current location to the destination. In anembodiment, the predefined precision range of the current spatiallocation of the user is 10 cm-20 cm which means last meter precision ispossible with the system of present disclosure. In an embodiment, theaugmented reality works as an integration of accelerometer, gyroscopeand a camera which detects inertial motion or movement of a mobiledevice (e.g., smartphone) in space with the user, wherein the mobiledevice is used for implementing the system 100. Here, movement of themobile device is recognized by a plurality of data received from theplurality of sensors 108 and a plurality of images simultaneouslycaptured by a camera frame used to mark feature points in the nestedenvironment. Further, the plurality of data is stored and periodicallyoverwritten by algorithm of the augmented reality technique anddisplacement in the marked feature points in the nested environment helpin detecting motion of the mobile device, thus, gives tracking of thecurrent spatial location of the user in real time without any dependencyon the internet.

In an embodiment, the step of tracking includes avoiding disorientationand side wall bumping of the user based on a deviation in direction ofthe user from a pre-planned direction on the optimal path. In otherwords, once navigational session starts during tracking of the currentspatial location of the user, it is determined using a magnetometer ofthe mobile device if the user starts moving in a direction of theoptimal path and direction of the user facing is captured and compared.In normal scenarios, the pre-planned direction on the optimal pathshould help the user to remain in centre of the optimal path. If theuser falls out of a range of direction deflection, then he/she tends tobump in the side walls. As a measure to avoid the disorientation andside wall bumping, an alert is generated immediately to get the user(especially visually challenged users) back in direction the path isleading towards.

Referring to FIG. 2 , at step 212, the one or more hardware processors104 are configured to dynamically update the optimal path from thetracked current spatial location of the user to the destination based onfeedback obtained from one or more user interaction modalities. Thefeedback obtained from one or more user interaction modalities help inredirecting the user in case of any turnings, obstacles present, anddeviation from the optimal path by alarming the user and thereby updatethe optimal path. For example, if an obstacle is found on the optimalpath which blocks the optimal path, then path regeneration is triggeredto help the users avoid the optimal path. Further, a crowded area withinan indoor environment can be used as an input for the system of thepresent disclosure to regenerate a path for the user avoiding thecrowded area thus helping in the path planning. This is particularlyhelpful for people with disability as they usually show reluctance incrossing crowded areas in order to avoid accidents and unwantedattention. In an embodiment, a multi-modal conversational interface isprovided to address communication challenges of the users with differentdisabilities by choosing appropriate user interaction modality, whereinthe user interaction modality is selected based on the user profilinginformation. In an embodiment, the one or more hardware processors areconfigured to detect one or more obstacles present on the optimal pathusing an obstacle detector. Here, the obstacle detector detects one ormore obstacles using a combination of (i) computer vision techniquesutilizing a plurality of data continuously captured by a camera todetect the one or more obstacles with a first range of view and (ii) oneor more ultrasonic sensors to detect the one or more obstacles with asecond range of view. For example, as a navigational session starts, thecamera and the one or more ultrasonic sensor start capturing data. Theplurality of data captured by the camera is used to provide informationof an upcoming obstacle falling on the optimal path to the user. Theinformation of the upcoming obstacle may include but not limited todistance of the obstacle from the user, what the obstacle is and thelike. The plurality of data continuously captured by the camera helps indetecting the upcoming obstacles with a first range of view(alternatively referred as a broader range of view). In an embodiment,the upcoming obstacles do not include the obstacles that are lying on afloor only but also the obstacles that are not placed on the floor suchas an open window, swinging door, head/chest level obstacles that mightnot be realised by a person with visual impairment using a cane alone,and the like. Here the first range of view refers to a distance up tillwhich the camera can capture which is determined using a pixel baseddistance measuring for depth measurement along with computer vision forobject detection. Further, the one or more ultrasonic sensors detect theone or more obstacles with a second range of view. Here, the secondrange of view refers to a smaller area range around the user (e.g. nearproximity of the user to 2-3 m) to provide immediate alerts for lessdistance obstacles.

Now, as the user moves in the indoor environment, then using theplurality of data captured using the camera, the one or more obstaclesare added as upcoming obstacle before even the one or more obstaclescome in proximity of the user. While in case of the one or moreultrasonic sensors, if any immediate obstacle comes in proximity (e.g.,something falls in the way, a person suddenly comes in front) of theuser, it is detected. In other words, when the user moves, then anyobstacle present on the optimal path is captured even before the userphysically arrives near the obstacle and the system of the presentdisclosure would have apriori knowledge that such an obstacle would fallat a given distance from where it was captured. This way a comprehensivefeedback for upcoming obstacle awareness and immediate obstacle alert iscovered along with distance from the obstacle and information of theobstacle. In an embodiment, the obstacle detector could be standalonesmall device connected to the mobile device implementing the system 100and can be carried by the user in pocket.

In an embodiment, the feedback obtained from the one or more userinteraction modalities for dynamically updating the optimal path include(i) feedback obtained from the obstacle detector, haptic feedback, voiceinstructions-based feedback, and visual interactions-based feedback. Forexample, a deaf user is not given voice instructions-based feedback, theselected user interaction modality for giving feedback to a deaf usermay include visual interaction-based feedback, haptic feedback such asbeep or vibrations, and feedback obtained from the obstacle detector forobstacle avoidance if any obstacle is present on the optimal path.Similarly, the selected user interaction modality for giving feedback toa blind user may include voice instructions-based feedback, hapticfeedback such as beep or vibrations, and feedback obtained from theobstacle detector.

FIG. 3 illustrates a non-limiting example of railway journey adaption tothe system and method for performing inclusive indoor navigation, inaccordance with some embodiments of the present disclosure. The systemof the present disclosure can be added with different features andutilities keeping core navigation solution intact for any indoorenvironment without any changes to be made to the indoor environment.Referring to FIG. 3 , for an integrated railway station, with the systemof the present disclosure, the user can get real time updates of train,ask train related queries and in case of emergency contact RPF, checkfood pantry and so on. Further, based on online booked ticket, thesystem of present disclosure is capable of automatically fetchingdetails and providing suggestion on some attractions of the railwaystation based on remaining time before arrival of the train or guide theuser to a specific platform/train coach in real time. The system ofpresent disclosure also provides landmark guidance to few areas suchplatform of the user's train, exact compartment of the user's train,accessible washroom, food court, drinking water, book stalls with highprecision and keep track of the user's luggage using RFID tags duringjourney.

FIG. 4 illustrates a non-limited example of a mall adapted to the systemand method for performing inclusive indoor navigation, in accordancewith some embodiments of the present disclosure. As depicted in FIG. 4 ,as the user walks in a mall and inputs a desired destination, the systemof the present disclosure determines an optimal path which is convenientwith respect to distance from an initial location of the user to thedestination, time to reach the destination and user profile. On user'sway, different offers going on in different stores can be notified andafter leaving an outlet, the user can be suggested for similar outletsin the same mall. Similarly, multiple other features depending onenvironment can be added to core solution.

FIG. 5 illustrates a non-limiting example of academic institutionsadapted to the system and method for performing inclusive indoornavigation, in accordance with some embodiments of the presentdisclosure. The present disclosure would serve as a social contributionfor students with disabilities and in providing common ground for bothmainstream as well disabled students. For example, as shown in FIG. 5 ,as a disabled user walks in campus, a categorized view ofinstitute/campus is provided which includes interconnected shops,canteens, washroom assistance and so on. Further, implementation of thesystem of present disclosure in such environments motivates people withdisabilities and overcome their everyday challenge of navigation forwhich one needs to depend on others.

In an embodiment, the system of the present disclosure can be added withdifferent features and utilities keeping core navigation solution intactfor any indoor environment without any changes to be made to the indoorenvironment. Few high impact non-limiting use cases include retail,public transport, public buildings wherein retail further includesmalls, supermarket and theatres. Similarly, public transport includesrailway stations, airport and bus stations. Public buildings may includehospitals wherein the system of the present disclosure can be integratedwith hospital system. For example, a user enters hospital then as perthe user's medical case, he/she is guided to respective doctor. Further,as the doctor updates in his/her system the required tests, the systemof the present disclosure guides the user to respective labs. The publicbuildings use cases may further include government offices, museums,amusement parks, and/or the like. Since the system of the presentdisclosure tracks the user in indoor space, thus for a fire evacuationuse case, there could be two ways including proactive support and rescueteam help. Firstly, the system of the present disclosure can notify theuser to nearest escape and secondly, it may help rescue teams to reachand identify the user within the indoor space to rescue.

In an embodiment, the system of present disclosure provides the userwith an additional favourite's functionality in which the user can marka few destinations in his/her favourite's list. For example, a user cansave his/her cabin in his/her favourite list as ‘work’ that may allowhim/her to not enter his/her cabin location as destination every timerather he/she can directly ask for route of ‘work’.

The present disclosure for performing inclusive indoor navigation isindependent of internet as it performs all the processing on a mobile orhandheld device itself. This reduces time of whole navigationalexperience as real time data is being used and processed on the mobiledevice itself without first going to a server then processing the dataand then returning output to the mobile device. The system of presentdisclosure provides a companion guidance which is an additional featureto give a human touch to the system creating a higher user engagement.The system of present disclosure includes virtual companion whichcommunicates with the user.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

It is to be understood that the scope of the protection is extended tosuch a program and in addition to a computer-readable means having amessage therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software modules located therein. Thus, themeans can include both hardware means, and software means. The methodembodiments described herein could be implemented in hardware andsoftware. The device may also include software means. Alternatively, theembodiments may be implemented on different hardware devices, e.g. usinga plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various modules described herein may be implemented in other modulesor combinations of other modules. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan comprise, store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

The illustrated steps are set out to explain the exemplary embodimentsshown, and it should be anticipated that ongoing technologicaldevelopment will change the manner in which particular functions areperformed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of thefunctional building blocks have been arbitrarily defined herein for theconvenience of the description. Alternative boundaries can be defined solong as the specified functions and relationships thereof areappropriately performed. Alternatives (including equivalents,extensions, variations, deviations, etc., of those described herein)will be apparent to persons skilled in the relevant art(s) based on theteachings contained herein. Such alternatives fall within the scope andspirit of the disclosed embodiments. Also, the words “comprising,”“having,” “containing,” and “including,” and other similar forms areintended to be equivalent in meaning and be open ended in that an itemor items following any one of these words is not meant to be anexhaustive listing of such item or items, or meant to be limited to onlythe listed item or items. It must also be noted that as used herein andin the appended claims (when included in the specification), thesingular forms “a,” “an,” and “the” include plural references unless thecontext clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include randomaccess memory (RAM), read-only memory (ROM), volatile memory,nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, andany other known physical storage media.

It is intended that the disclosure and examples be considered asexemplary only, with a true scope indicated by the following claims.

What is claimed is:
 1. A processor implemented method, comprising:obtaining, by one or more hardware processors, nested environment dataof a facility for indoor navigation performed by a user, wherein thenested environment data is stored on and retrieved from a server, andthe nested environment data of the facility is obtained by: evaluating aplurality of floor exit maps of each floor of a plurality of floorsagainst an evaluation criterion, wherein the evaluation criterion isused to check real world architectural details of the facility;creating, based on a mismatch between the plurality of floor exit mapsand the evaluation criterion, a two-dimensional digital map of eachfloor among the plurality of floors of the facility; sequentiallyarranging the created two-dimensional digital map of each floor of theplurality of floors of the facility; determining, based on thesequential arrangement, a nested map of the facility; and performing maplabelling to localize and capture information of a plurality oflandmarks in the nested map, wherein the map labelling refers to addingdetails to the plurality of landmarks with respect to the createdtwo-dimensional digital map along with capturing surrounding informationof the plurality of landmarks, each landmark of the plurality oflandmarks is tagged to a reference surroundings based on one or moreparameters in the created two-dimensional digital map, the one or moreparameters include images of the plurality of landmarks, text orsignages of the plurality of landmarks, a direction of the plurality oflandmarks, and a Wireless Fidelity (Wi-Fi) signal strength and amagnetic field intensity of the plurality of landmarks; receiving, bythe one or more hardware processors, a destination location within thefacility from the user; estimating, using a surrounding recognitionmachine learning model implemented by the one or more hardwareprocessors, a current spatial location of the user with a predefinedprecision range at centimeter (cm) level by identifying (i) a userspecific area in the facility using the surrounding recognition machinelearning model trained with a plurality of real world images of thefacility and (ii) the current spatial location of the user with respectto the identified user specific area by triangulating input datareceived from a plurality of sensors; determining, by the one or morehardware processors, an optimal path from the current spatial locationto the destination location using the nested environment data, whereinthe optimal path from the current spatial location to the destinationlocation is categorized as (i) a convenient path (ii) amulti-destination path, or (iii) a shortest path in accordance with oneor more user constraints, wherein the convenient path is a path withmultiple destinations, the one or more user constraints include userprofiling information, the convenient path is selected based on the oneor more user constraints, a minimum time from the current spatiallocation to the destination location, and a minimum distance from thecurrent spatial location to the destination location, themulti-destination path is a path that is longest with respect to a timeand a distance and covers all important landmarks within the facility,the shortest path is a path that is shortest with respect to the timeand the distance from the current spatial location to the destinationlocation, and the user profiling information refers to details ofphysical appearance of the user and is indicative of whether the user isa physically fit person without any disability; tracking, using anaugmented reality technique implemented by the one or more hardwareprocessors, the current spatial location of the user while the usernavigates on the optimal path from the current spatial location to thedestination location; and detecting one or more obstacles present on theoptimal path using an obstacle detector coupled with the one or morehardware processors, wherein the obstacle detector detects the one ormore obstacles using a combination of: (i) computer vision techniquesutilizing data continuously captured by a camera to detect the one ormore obstacles with a first range of view, and (ii) one or moreultrasonic sensors to detect the one or more obstacles with a secondrange of view, wherein the second range of view refers to a small arearange around the user to provide immediate alerts for less distanceobstacles, and the small area range is 2 meters-3 meters; determining,by the one or more hardware processors, based on the data that iscontinuously captured by the camera, a pattern of the one or moreobstacles; providing, by the one or more hardware processors, thedetermined pattern as input to the one or more ultrasonic sensors;predicting, via the one or more ultrasonic sensors coupled with by theone or more hardware processors, one or more incoming obstacles based onthe determined pattern, wherein the one or more incoming obstacles aredifferent from the detected one or more obstacles; and dynamicallyupdating, by the one or more hardware processors, the optimal path fromthe tracked current spatial location of the user to the destinationlocation based on a feedback obtained from one or more user interactionmodalities.
 2. The method of claim 1, wherein the one or more userconstraints further include information of landmark of interest, adistance from an initial location of the user to the destinationlocation, and the time to reach the destination location.
 3. The methodof claim 1, wherein the predefined precision range of the currentspatial location of the user is between 10 cm to 20 cm.
 4. The method ofclaim 1, further comprising: detecting a deviation in a direction of theuser from a preplanned direction on the optimal path; and detecting anobstacle in the optimal path of the user based on the detected deviationin the direction of the user from the preplanned direction on theoptimal path.
 5. The method of claim 1, wherein the feedback obtainedfrom the one or more user interaction modalities includes a feedbackobtained from the obstacle detector, a haptic feedback, a voiceinstructions-based feedback, and a visual interactions-based feedback.6. A system comprising: one or more data storage devices operativelycoupled to the one or more hardware processors and configured to storeinstructions configured for execution via the one or more hardwareprocessors to: obtain nested environment data of a facility for indoornavigation performed by a user, wherein the nested environment data isstored on and retrieved from a server, and the nested environment dataof the facility is obtained by: evaluating a plurality of floor exitmaps of each floor of a plurality of floors against an evaluationcriterion, wherein the evaluation criterion is used to check real worldarchitectural details of the facility; creating, based on a mismatchbetween the plurality of floor exit maps and the evaluation criterion, atwo-dimensional digital map of each floor among the plurality of floorsof the facility; sequentially arranging the created two-dimensionaldigital map of each floor of the plurality of floors of the facility;determining, based on the sequential arrangement, a nested map of thefacility; and performing map labelling to localize and captureinformation of a plurality of landmarks in the nested map, wherein  themap labelling refers to adding details to the plurality of landmarkswith respect to the created two-dimensional digital map along withcapturing surrounding information of the plurality of landmarks,  eachlandmark of the plurality of landmarks is tagged to a referencesurroundings based on one or more parameters in the createdtwo-dimensional digital map,  the one or more parameters include imagesof the plurality of landmarks, text or signages of the plurality oflandmarks, a direction of the plurality of landmarks, and a WirelessFidelity (Wi-Fi) signal strength and a magnetic field intensity of theplurality of landmarks; receive a destination location within thefacility from the user; estimate, using a surrounding recognitionmachine learning model, a current spatial location of the user with apredefined precision range at centimeter (cm) level by identifying (i) auser specific area in the facility using the surrounding recognitionmachine learning model trained with a plurality of real world images ofthe facility and (ii) the current spatial location of the user withrespect to the identified user specific area by triangulating input datareceived from a plurality of sensors; determine an optimal path from thecurrent spatial location to the destination location using the nestedenvironment data, wherein the optimal path from the current spatiallocation to the destination location is categorized as (i) a convenientpath (ii) a multi-destination path, or (iii) a shortest path inaccordance with one or more user constraints, wherein the convenientpath is a path with multiple destinations, the one or more userconstraints include user profiling information, the convenient path isselected based on the one or more user constraints, a minimum time fromthe current spatial location to the destination location, and a minimumdistance from the current spatial location to the destination location,the multi-destination path is a path that is longest with respect to atime and a distance and covers all important landmarks within thefacility, the shortest path is a path that is shortest with respect tothe time and the distance from the current spatial location to thedestination location, and the user profiling information refers todetails of physical appearance of the user and is indicative of whetherthe user is a physically fit person without any disability; track, usingan augmented reality technique, the current spatial location of the userwhile the user navigates on the optimal path from the current spatiallocation to the destination location; and detect one or more obstaclespresent on the optimal path using an obstacle detector, wherein theobstacle detector detects the one or more obstacles using: (i) computervision techniques utilizing data continuously captured by a camera todetect the one or more obstacles with a first range of view, and (ii)one or more ultrasonic sensors to detect the one or more obstacles witha second range of view, wherein the second range of view refers to asmall area range around the user to provide immediate alerts for lessdistance obstacles, and the small area range is 2 meters-3 meters;determine, based on the data that is continuously captured by thecamera, a pattern of the one or more obstacles; provide the determinedpattern as input to the one or more ultrasonic sensors; predict, via theone or more ultrasonic sensors, one or more incoming obstacles based onthe provided pattern, wherein the one or more incoming obstacles aredifferent from the detected one or more obstacles; and dynamicallyupdate the optimal path from the tracked current spatial location of theuser to the destination location based on a feedback obtained from oneor more user interaction modalities.
 7. The system of claim 6, whereinthe one or more user constraints further information of landmark ofinterest, a distance from an initial location of the user to thedestination location, and a time to reach the destination location. 8.The system of claim 6, wherein the predefined precision range of thecurrent spatial location of the user is between 10 cm to 20 cm.
 9. Thesystem of claim 6, wherein the one or more hardware processors arefurther configured to: detect a deviation in a direction of the userfrom a preplanned direction on the optimal path; and detect an obstaclein the optimal path of the user based on the detected deviation in thedirection of the user from the preplanned direction on the optimal path.10. The system of claim 6, wherein the feedback obtained from the one ormore user interaction modalities includes a feedback obtained from theobstacle detector, haptic feedback, voice instructions-based feedback,and visual interactions-based feedback.
 11. One or more non-transitorymachine readable information storage mediums comprising one or moreinstructions which when executed by one or more hardware processorscauses: Obtaining nested environment data of a facility for indoornavigation performed by a user, wherein the nested environment data isstored on and retrieved from a server, and the nested environment dataof the facility is obtained by: evaluating a plurality of floor exitmaps of each floor of a plurality of floors against an evaluationcriterion, wherein the evaluation criterion is used to check real worldarchitectural details of the facility; creating, based on a mismatchbetween the plurality of floor exit maps and the evaluation criterion, atwo-dimensional digital map of each floor among the plurality of floorsof the facility; sequentially arranging the created two-dimensionaldigital map of each floor of the plurality of floors of the facility;determining, based on the sequential arrangement, a nested map of thefacility; and performing map labelling to localize and captureinformation of a plurality of landmarks in the nested map, wherein themap labelling refers to adding details to the plurality of landmarkswith respect to the created two-dimensional digital map along withcapturing surrounding information of the plurality of landmarks, eachlandmark of the plurality of landmarks is tagged to a referencesurroundings based on one or more parameters in the createdtwo-dimensional digital map, the one or more parameters include imagesof the plurality of landmarks, text or signages of the plurality oflandmarks, a direction of the plurality of landmarks, and a WirelessFidelity (Wi-Fi) signal strength and a magnetic field intensity of theplurality of landmarks; receiving a destination location within thefacility from the user; estimating using a surrounding recognitionmachine learning model implemented, a current spatial location of theuser with a predefined precision range at centimeter (cm) level byidentifying (i) a user specific area in the facility using thesurrounding recognition machine learning model trained with a pluralityof real world images of the facility and (ii) the current spatiallocation of the user with respect to the identified user specific areaby triangulating input data received from a plurality of sensors;determining, an optimal path from the current spatial location to thedestination location using the nested environment data, wherein theoptimal path from the current spatial location to the destinationlocation is categorized as (i) a convenient path (ii) amulti-destination path, or (iii) a shortest path in accordance with oneor more user constraints, wherein the convenient path is a path withmultiple destinations, the one or more user constraints include userprofiling information, the convenient path is selected based on the oneor more user constraints, a minimum time from the current spatiallocation to the destination location, and a minimum distance from thecurrent spatial location to the destination location, themulti-destination path is a path that is longest with respect to a timeand a distance and covers all important landmarks within the facility,the shortest path is a path that is shortest with respect to the timeand the distance from the current spatial location to the destinationlocation, and the user profiling information refers to details ofphysical appearance of the user and is indicative of whether the user isa physically fit person without any disability; tracking, using anaugmented reality technique implemented, the current spatial location ofthe user while the user navigates on the optimal path from the currentspatial location to the destination location; and detecting one or moreobstacles present on the optimal path using an obstacle detector,wherein the obstacle detector detects the one or more obstacles using acombination of: (i) computer vision techniques utilizing datacontinuously captured by a camera to detect the one or more obstacleswith a first range of view, and (ii) one or more ultrasonic sensors todetect the one or more obstacles with a second range of view, whereinthe second range of view refers to a small area range around the user toprovide immediate alerts for less distance obstacles, and the small arearange is 2 meters-3 meters; determining, based on the data that iscontinuously captured by the camera, a pattern of the one or moreobstacles; providing the determined pattern as input to the one or moreultrasonic sensors; predicting, via the one or more ultrasonic sensors,one or more incoming obstacles based on the determined pattern, whereinthe one or more incoming obstacles are different from the detected oneor more obstacles; and dynamically updating, the optimal path from thetracked current spatial location of the user to the destination locationbased on a feedback obtained from one or more user interactionmodalities.